Most RAG systems don’t understand sophisticated documents — they shred them
Dippu Kumar Singh
January 31, 2026
RAG shredding
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By now, many enterprises have deployed some form of RAG. The promise is seductive: index your PDFs, connect an LLM and instantly democratize your corporate knowledge.

But for industries dependent on heavy engineering, the reality has been underwhelming. Engineers ask specific questions about infrastructure, and the bot hallucinates.

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The failure isn't in the LLM. The failure is in the preprocessing.

Standard RAG pipelines treat documents as flat strings of text. They use "fixed-size chunking" (cutting a document every 500 characters). This works for prose, but it destroys the logic of technical manuals. It slices tables in half, severs captions from images, and ignores the visual hierarchy of the page.


Improving RAG reliability isn't about buying a bigger model; it's about fixing the "dark data" problem through semantic chunking and multimodal textualization.

Here is the architectural framework for building a RAG system that can actually read a manual.

The fallacy of fixed-size chunking
In a standard Python RAG tutorial, you split text by character count. In an enterprise PDF, this is disastrous.

If a safety specification table spans 1,000 tokens, and your chunk size is 500, you have just split the "voltage limit" header from the "240V" value. The vector database stores them separately. When a user asks, "What is the voltage limit?", the retrieval system finds the header but not the value. The LLM, forced to answer, often guesses.